1. Technical Field
The present invention relates to iris recognition and more particularly to an efficient and accurate iris sensor and recognition system and method.
2. Description of the Related Art
Very high information content exists in iris texture. This makes the iris attractive for large scale identification systems with possibly millions of people. However, conventional identification systems typically operate by performing N 1:1 matches of a probe against a database. This can get prohibitively expensive in terms of computation as N grows large. Note that for identification systems the per-match time dominates system performance especially when the gallery size exceeds the feature size, unlike verification where feature extraction time is the primary component.
The iris is rich in textural features which can be used to describe individuals for identification. The iris can be segmented and unwrapped into a rectangular image. From the unwrapped iris, texture is extracted by applying a Gabor filter bank. This is encoded into a binary image, known as the iris code, which serves as the feature vector for recognition. Personal identity is determined by measuring the distance between two such feature vectors.
In an identification system, one iris code (the probe) is matched to an entire database of iris codes (the gallery). If the distance between the probe and gallery is below a certain threshold, the probe and gallery are said to match and be of the same person. This matching scheme is known as a 1:N system because the probe is matched to N gallery instances. The computational requirement of an identification system is dependent on the size of the input iris code, the number of instances in the gallery, and an alignment search range. In many cases, the size of the gallery can grow so large that real-time recognition quickly becomes expensive. Systems with 200 billion iris code comparisons are possible.
Several approaches have been taken to speed up the time taken for iris recognition. In one system, the recognition workload is spread across a set of processes. Iris textures are unwrapped and aligned using Fourier components of the iris texture rather than full search. Other work aims at reducing the size of the feature vector for recognition. Genetic algorithms are used to reduce the feature space of an iris code. PCA is used to reduce the size of the iris representation. In addition, each probe is assigned to an inductively learned cluster and then only gallery codes from the same cluster are tested.